Results 11 to 20 of about 3,863,679 (333)
Journal of Plant Ecology, 2023 glmm.hp is an R package designed to evaluate the relative importance of collinear predictors within generalized linear mixed models (GLMMs). Since its initial release in January 2022, it has rapidly gained recognition and popularity among ecologists ...
Jiangshan Lai +3 more
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OpenChat: Advancing Open-source Language Models with Mixed-Quality Data [PDF]
International Conference on Learning Representations, 2023 Nowadays, open-source large language models like LLaMA have emerged. Recent developments have incorporated supervised fine-tuning (SFT) and reinforcement learning fine-tuning (RLFT) to align these models with human goals.
Guan Wang +5 more
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Bayes Factors for Mixed Models
Computational Brain & Behavior, 2021 Although Bayesian linear mixed effects models are increasingly popular for analysis of within-subject designs in psychology and other fields, there remains considerable ambiguity on the most appropriate Bayes factor hypothesis test to quantify the degree
J. V. van Doorn +4 more
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Fitting Linear Mixed-Effects Models Using lme4 [PDF]
, 2014 Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in
D. Bates +3 more
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RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models
Bioinform., 2006 UNLABELLED RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML).
A. Stamatakis
semanticscholar +1 more source
Flexible semiparametric mixed models [PDF]
, 2005 In linear mixed models the influence of covariates is restricted to a strictly parametric form. With the rise of semi- and nonparametric regression also the mixed model has been expanded to allow for additive predictors.
Reithinger, Florian, Tutz, Gerhard
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Multiple testing correction in linear mixed models. [PDF]
, 2016 BackgroundMultiple hypothesis testing is a major issue in genome-wide association studies (GWAS), which often analyze millions of markers. The permutation test is considered to be the gold standard in multiple testing correction as it accurately takes ...
Eskin, Eleazar +3 more
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Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R
Behavior Research Methods, 2020 Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly rejects the null hypothesis.
Levi Kumle, M. Võ, Dejan Draschkow
semanticscholar +1 more source
Age and diet shape the genetic architecture of body weight in diversity outbred mice
eLife, 2022 Understanding how genetic variation shapes a complex trait relies on accurately quantifying both the additive genetic and genotype–environment interaction effects in an age-dependent manner. We used a linear mixed model to quantify diet-dependent genetic
Kevin M Wright +6 more
doaj +1 more source
Proceedings of the National Academy of Sciences, 2003 Markov random fields can encode complex probabilistic relationships involving multiple variables and admit efficient procedures for probabilistic inference. However, from a knowledge engineering point of view, these models suffer from a serious limitation.
openaire +3 more sources